MRI Verilerinde Tümör Tespiti için Transfer Tabanlı Derin Öğrenme Algoritması Karşılaştırması

Year 2024, , 1322 - 1339, 15.09.2024

Cem Demirel , Emel Soylu

https://doi.org/10.31466/kfbd.1455542

Abstract

Bu çalışmada, Manyetik Rezonans Görüntüleme (MRG), invazif olmayan doğası ve yüksek çözünürlüklü görüntüleme yetenekleri nedeniyle beyin tümörlerinin teşhisinde hayati bir araç haline gelmiştir. Bu çalışmada, derin öğrenme algoritmalarının performanslarını karşılaştırdık. Kapsamlı bir MRG taramaları veri kümesi, modelimizi eğitmek ve doğrulamak için kullanıldı, bu da çeşitli tümör tipleri ve görüntüleme koşulları için sağlam bir performans sağladı. Sonuçlar, yakalama konusunda yüksek bir doğruluk ve hassasiyet elde ederek yaklaşımımızın etkinliğini göstermektedir. Çalışmamız, nöro görüntüleme alanında erken teşhis ve takip için etkili ve güvenilir araçların geliştirilmesine katkıda bulunmaktadır. Bulgularımız, beyin MRG görüntü sınıflandırma görevleriyle uğraşırken uygun bir derin sinir ağı mimarisi seçmenin önemini vurgular. DenseNet-121, doğru ve güvenilir sınıflandırma için sağlam bir seçenek olarak ortaya çıkıyor ve klinik teşhis ve tıbbi görüntüleme alanlarında potansiyel uygulamalar sunuyor. Sonuç olarak, çalışmamız, MRG'nin beyin tümörü teşhisi açısından önemini ve derin öğrenme algoritmalarının doğruluğu ve hassasiyeti artırmadaki potansiyelini vurgular. DenseNet-121'e dayalı yaklaşımımız, nöro görüntüleme alanında hastaların bakımını ve sonuçlarını iyileştirmeye katkıda bulunarak klinik teşhis ve tıbbi görüntüleme uygulamaları için umut vaat etmektedir.

Keywords

Beyin tümörü, MRI taramaları, Yapay zeka, Bilgisayar destekli görüntü analizleri

Analysis of Transfer Learning-Based Algorithms for Tumor Detection in Medical Imaging Data

Abstract

Magnetic Resonance Imaging (MRI) has become a vital tool in the diagnosis of brain tumors due to its non-invasive nature and high-resolution imaging capabilities. In this study, we compared the performances of deep learning algorithms. A comprehensive dataset of MRI scans was utilized to train and validate our model, ensuring robust performance across various tumor types and imaging conditions. The results demonstrate the effectiveness of our approach, achieving a high level of accuracy and sensitivity in tumor detection. Our work contributes to the development of efficient and reliable tools for early diagnosis and monitoring of brain tumors, ultimately enhancing patient care and outcomes in the field of neuroimaging. Our findings highlight the significance of selecting an appropriate deep neural network architecture when dealing with brain MRI image classification tasks. DenseNet-121 emerges as a robust choice for accurate and reliable classification, offering potential applications in clinical diagnostics and medical imaging. In conclusion, our study underscores the importance of MRI in brain tumor diagnosis and the potential of deep learning algorithms to enhance accuracy and sensitivity. Our approach, based on DenseNet-121, holds promise for clinical diagnostics and medical imaging applications, contributing to improved patient care and outcomes in neuroimaging.

Keywords

Brain tumor, MRI scans, Artificial intelligence, Computer-assisted image analyses

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